Searching In One Billion Vectors Re-rank With Source Coding
Jégou Hervé Inria - Irisa, Tavenard Romain Inria - Irisa, Douze Matthijs Inria Rhône-alpes / Ljk Laboratoire Jean Kuntzmann, Sed, Amsaleg Laurent Inria - Irisa. Arxiv 2011
Recent indexing techniques inspired by source coding have been shown successful to index billions of high-dimensional vectors in memory. In this paper, we propose an approach that re-ranks the neighbor hypotheses obtained by these compressed-domain indexing methods. In contrast to the usual post-verification scheme, which performs exact distance calculation on the short-list of hypotheses, the estimated distances are refined based on short quantization codes, to avoid reading the full vectors from disk. We have released a new public dataset of one billion 128-dimensional vectors and proposed an experimental setup to evaluate high dimensional indexing algorithms on a realistic scale. Experiments show that our method accurately and efficiently re-ranks the neighbor hypotheses using little memory compared to the full vectors representation.
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